Neuroimaging Category
Validation of group-wise registration for surface-based functional MRI analysis
Oct. 20, 2020—Chang Yu, Yue Liu, Leon Cai, Cailey Kerley, Kaiwen Xu, Katherine Aboud, Warren Taylor, Hakmook Kang, Andrea Shafer, Lori Beason-Held, Susan Resnick, Bennett Landman, Ilwoo Lyu. “Validation of group-wise registration for surface-based functional MRI analysis”. SPIE Medical Imaging 2021. [Full text][Code] Abstract Resting-state functional MRI (rsfMRI) provides important information for studying and mapping the activities...
Automatic Labeling of Cortical Sulci using Convolutional Neural Networks in a Developmental Cohort
Apr. 20, 2020—Lingyan Hao, Shunxing Bao, Yucheng Tang, Riqiang Gao, Prasanna Parvathaneni, Jacob Miller, Willa Voorhies, Jewelia Yao, Silvia Bunge, Kevin Weiner, Bennett Landman, Ilwoo Lyu. “Automatic Labeling of Cortical Sulci using Convolutional Neural Networks in a Developmental Cohort”. IEEE International Symposium on Biomedical Imaging (ISBI) 2020, IEEE, 412-415, Iowa City, Iowa, USA, 2020. [Full text][Code] Abstract...
Enabling Multi-shell b-Value Generalizability of Data-Driven Diffusion Models with Deep SHORE
Jan. 17, 2020—Nath V, Lyu I, Schilling KG, Parvathaneni P, Hansen CB, Huo Y, Janve VA, Gao Y, Stepniewska I, Anderson AW, Landman BA. Enabling Multi-shell b-Value Generalizability of Data-Driven Diffusion Models with Deep SHORE. In International Conference on Medical Image Computing and Computer-Assisted Intervention 2019 Oct 13 (pp. 573-581). Springer, Cham. Full text: https://arxiv.org/ftp/arxiv/papers/1907/1907.06319.pdf Abstract Intra-voxel...
Harmonizing 1.5 T/3T diffusion weighted MRI through development of deep learning stabilized microarchitecture estimators
Jan. 17, 2020—Nath V, Remedios S, Parvathaneni P, Hansen CB, Bayrak RG, Bermudez C, Blaber JA, Schilling KG, Janve VA, Gao Y, Huo Y. Harmonizing 1.5 T/3T diffusion weighted MRI through development of deep learning stabilized microarchitecture estimators. In Medical Imaging 2019: Image Processing 2019 Mar 15 (Vol. 10949, p. 109490O). International Society for Optics and Photonics....
Tractography reproducibility challenge with empirical data (TRAceD): The 2017 ISMRM diffusion study group challenge
Jan. 17, 2020—Nath V, Schilling KG, Parvathaneni P, Huo Y, Blaber JA, Hainline AE, Barakovic M, Romascano D, Rafael‐Patino J, Frigo M, Girard G. Tractography reproducibility challenge with empirical data (traced): The 2017 ISMRM diffusion study group challenge. Journal of Magnetic Resonance Imaging. 2020 Jan;51(1):234-49. Full text: https://www.ncbi.nlm.nih.gov/pubmed/31179595 Abstract BACKGROUND: Fiber tracking with diffusion-weighted MRI has become an...
Deep learning reveals untapped information for local white-matter fiber reconstruction in diffusion-weighted MRI
Jan. 17, 2020—Nath V, Schilling KG, Parvathaneni P, Hansen CB, Hainline AE, Huo Y, Blaber JA, Lyu I, Janve V, Gao Y, Stepniewska I, Anderson AW, Landman BA. Deep learning reveals untapped information for local white-matter fiber reconstruction in diffusion-weighted MRI. Magnetic resonance imaging. 2019 Oct 1;62:220-7. Abstract PURPOSE: Diffusion-weighted magnetic resonance imaging (DW-MRI) is of critical importance...
Generalizing Deep Whole Brain Segmentation for Pediatric and Post-Contrast MRI with Augmented Transfer Learning
Aug. 13, 2019—Bermudez, C., Blaber, J., Remedios, S.W., Reynolds, J.E., Lebel, C., McHugo, M., Heckers, S., Huo, Y., Landman, B.A. Generalizing Deep Whole Brain Segmentation for Pediatric and Post-Constrast MRI with Augmented Transfer Learning. SPIE Medical Imaging: Image Processing 2020. Houston, TX. Full Text: NIHMSID Abstract Generalizability is an important problem in deep neural networks, especially in...
Anatomical context improves deep learning on the brain age estimation task
Jul. 12, 2019—Bermudez, C., Plassard, A. J., Chaganti, S., Huo, Y., Aboud, K. E., Cutting, L. E., … & Landman, B. A. (2019). Anatomical context improves deep learning on the brain age estimation task. Magnetic Resonance Imaging. Full Text: https://www.ncbi.nlm.nih.gov/pubmed/31247249 Abstract Deep learning has shown remarkable improvements in the analysis of medical images without the need for...
Improved gray matter surface based spatial statistics in neuroimaging studies
May. 21, 2019—Prasanna Parvathaneni; Ilwoo Lyu; Yuankai Huo; Baxter P. Rogers; Kurt G. Schilling; Vishwesh Nath; Justin A Blaber; Allison E Hainline; Adam W Anderson; Neil D. Woodward; Bennett A Landman. “Improved gray matter surface based spatial statistics in neuroimaging studies.” Magnetic Resonance Imaging, 61, 285-295, 2019. Full text Abstract Neuroimaging often involves acquiring high-resolution anatomical images along with...
Learning 3D White Matter Microstructure from 2D Histology
Apr. 1, 2019—Histological analysis is typically the gold standard for validating measures of tissue microstructure derived from magnetic resonance imaging (MRI) contrasts. However, most histological investigations are inherently 2-dimensional (2D), due to increased field-of-view, higher in-plane resolutions, ease of acquisition, decreased costs, and a large number of available contrasts compared to 3-dimensional (3D) analysis. Because of this,...